@misc{RomeroZalizCantudoPerezetal., author = {Romero-Zaliz, Rocio and Cantudo, Antonio and P{\´e}rez, Eduardo and Jimenez-Molinos, Francisco and Wenger, Christian and Roldan, Juan Bautista}, title = {An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors}, series = {Electronics (MDPI)}, volume = {10}, journal = {Electronics (MDPI)}, number = {24}, issn = {2079-9292}, doi = {10.3390/electronics10243141}, abstract = {We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on HfO2 dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed thatNNaccuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc.}, language = {en} } @misc{StankeWengerBieretal., author = {Stanke, Sandra and Wenger, Christian and Bier, Frank F. and H{\"o}lzel, Ralph}, title = {AC electrokinetic immobilization of influenza virus}, series = {Electrophoresis}, volume = {43}, journal = {Electrophoresis}, number = {12}, issn = {1522-2683}, doi = {10.1002/elps.202100324}, pages = {1309 -- 1321}, abstract = {The use of alternating current (AC) electrokinetic forces, like dielectrophoresis and AC electroosmosis, as a simple and fast method to immobilize sub-micrometer objects onto nanoelectrode arrays is presented. Due to its medical relevance, the influenza virus is chosen as a model organism. One of the outstanding features is that the immobilization of viral material to the electrodes can be achieved permanently, allowing subsequent handling independently from the electrical setup. Thus, by using merely electric fields, we demonstrate that the need of prior chemical surface modification could become obsolete. The accumulation of viral material over time is observed by fluorescence microscopy. The influences of side effects like electrothermal fluid flow, causing a fluid motion above the electrodes and causing an intensity gradient within the electrode array, are discussed. Due to the improved resolution by combining fluorescence microscopy with deconvolution, it is shown that the viral material is mainly drawn to the electrode edge and to a lesser extent to the electrode surface. Finally, areas of application for this functionalization technique are presented.}, language = {en} } @misc{BischoffLeisePerezBoschQuesadaetal., author = {Bischoff, Carl and Leise, Jakob and Perez-Bosch Quesada, Emilio and P{\´e}rez, Eduardo and Wenger, Christian and Kloes, Alexander}, title = {Implementation of device-to-device and cycle-to-cycle variability of memristive devices in circuit simulations}, series = {Solid-State Electronics}, volume = {194}, journal = {Solid-State Electronics}, issn = {0038-1101}, doi = {10.1016/j.sse.2022.108321}, pages = {4}, abstract = {We present a statistical procedure for the extraction of parameters of a compact model for memristive devices. Thereby, in a circuit simulation the typical fluctuations of the current-voltage (I-V) characteristics from device-to-device (D2D) and from cycle-to-cycle (C2C) can be emulated. The approach is based on the Stanford model whose parameters play a key role to integrating D2D and C2C dispersion. The influence of such variabilities over the model's parameters is investigated by using a fitting algorithm fed with experimental data. After this, the statistical distributions of the parameters are used in a Monte Carlo simulation to reproduce the I-V D2D and C2C dispersions which show a good agreement to the measured curves. The results allow the simulation of the on/off current variation for the design of RRAM cells or memristor-based artificial neural networks.}, language = {en} } @misc{MannocciBaroniMelacarneetal., author = {Mannocci, Piergiulio and Baroni, Andrea and Melacarne, Enrico and Zambelli, Cristian and Olivo, Piero and P{\´e}rez, Eduardo and Wenger, Christian and Ielmini, Daniele}, title = {In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory}, series = {IEEE Nanotechnology Magazine}, volume = {16}, journal = {IEEE Nanotechnology Magazine}, number = {2}, issn = {1932-4510}, doi = {10.1109/MNANO.2022.3141515}, pages = {4 -- 13}, abstract = {In Memory Computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is principal component analysis (PCA), which heavily relies on matrixvector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMCbased PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching random-access memory (RRAM). The classification accuracy of the Wisconsin Breast Cancer data set reaches 95.43\%, close to floatingpoint implementation. Our simulations indicate a 250× improvement in energy efficiency compared to commercial GPUs, thus supporting IMC for energy-efficient ML in modern data-intensive computing.}, language = {en} } @misc{StrobelAlvaradoChavarinLeszczynskietal., author = {Strobel, Carsten and Alvarado Chavarin, Carlos and Leszczynski, Sebastian and Richter, Karola and Knaut, Martin and Reif, Johanna and V{\"o}lkel, Sandra and Albert, Matthias and Wenger, Christian and Bartha, Johann Wolfgang and Mikolajick, Thomas}, title = {Improved Graphene-base Heterojunction Transistor with Different Collector Semi-conductors for High-frequency Applications}, series = {Advanced Materials Letters}, volume = {13}, journal = {Advanced Materials Letters}, number = {1}, issn = {0976-3961}, doi = {10.5185/amlett.2022.011688}, abstract = {A new kind of transistor device with a graphene monolayer embedded between two n-type silicon layers is fabricated and characterized. The device is called graphene-base heterojunction transistor (GBHT). The base-voltage controls the current of the device flowing from the emitter via graphene to the collector. The transit time for electrons passing by the ultrathin graphene layer is extremely short which makes the device very promising for high frequency RF-electronics. The output current of the device is saturated and clearly modulated by the base voltage. Further, the silicon collector of the GBHT is replaced by germanium to improve the device performance. This enabled the collector current to be increased by almost three orders of magnitude. Also, the common-emitter current gain (Ic/Ib) increased from 10-3 to approximately 0.3 for the newly designed device. However, the ON-OFF ratio of the improved germanium based GBHT has so far been rather low. Further optimizations are necessary in order to fully exploit the potential of the graphene-base heterojunction transistor.}, language = {en} } @misc{FritscherKnoedtelMallahetal., author = {Fritscher, Markus and Kn{\"o}dtel, Johannes and Mallah, Maen and Pechmann, Stefan and Perez-Bosch Quesada, Emilio and Rizzi, Tommaso and Wenger, Christian and Reichenbach, Marc}, title = {Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks}, series = {Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science}, journal = {Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science}, publisher = {Springer}, isbn = {978-3-031-04579-0}, issn = {0302-9743}, doi = {10.1007/978-3-031-04580-6_27}, pages = {401 -- 417}, abstract = {Weight storage is a key challenge in the efficient implementation of artificial neural networks. Novel memory technologies such as RRAM are able to greatly improve density and introduce non-volatility and multibit capabilities to this component of ANN accelerators. The usage of RRAM in this domain comes with downsides, mainly caused by cycle-to-cycle and device-to-device variability leading to erroneous readouts, greatly affecting digital systems. ANNs have the ability to compensate for this by their inherent redundancy and usually exhibit a gradual deterioration in the accuracy of the task at hand. This means, that slight error rates can be acceptable for weight storage in an ANN accelerator. In this work we link device-to-device variability to the accuracy of an ANN for such an accelerator. From this study, we can estimate how strongly a certain net is affected by a certain device parameter variability. This methodology is then used to present three mitigation strategies and to evaluate how they affect the reaction of the network to variability: a) Dropout Layers b) Fault-Aware Training c) Redundancy. These mitigations are then evaluated by their ability to improve accuracy and to lower hardware overhead by providing data for a real-word example. We improved this network's resilience in such a way that it could tolerate double the variation in one of the device parameters (standard deviation of the oxide thickness can be 0.4 nm instead of 0.2 nm while maintaining sufficient accuracy.)}, language = {en} } @misc{MahadevaiahPerezLiskeretal., author = {Mahadevaiah, Mamathamba Kalishettyhalli and P{\´e}rez, Eduardo and Lisker, Marco and Schubert, Markus Andreas and Perez-Bosch Quesada, Emilio and Wenger, Christian and Mai, Andreas}, title = {Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers}, series = {Electronics : open access journal}, volume = {11}, journal = {Electronics : open access journal}, number = {10}, issn = {2079-9292}, doi = {10.3390/electronics11101540}, pages = {14}, abstract = {The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I-V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.}, language = {en} } @misc{GlukhovMiloBaronietal., author = {Glukhov, Artem and Milo, Valerio and Baroni, Andrea and Lepri, Nicola and Zambelli, Cristian and Olivo, Piero and P{\´e}rez, Eduardo and Wenger, Christian and Ielmini, Daniele}, title = {Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators}, series = {2022 IEEE International Reliability Physics Symposium (IRPS)}, journal = {2022 IEEE International Reliability Physics Symposium (IRPS)}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, isbn = {978-1-6654-7950-9}, issn = {2473-2001}, doi = {10.1109/IRPS48227.2022.9764497}, pages = {3C.3-1 -- 3C.3-7}, abstract = {Resistive-switching random access memory (RRAM) is a promising technology for in-memory computing (IMC) to accelerate training and inference of deep neural networks (DNNs). This work presents the first physics-based statistical model describing (i) multilevel RRAM device program/verify (PV) algorithms by controlled set transition, (ii) the stochastic cycle-to-cycle (C2C) and device-to-device (D2D) variations within the array, and (iii) the impact of such imprecisions on the accuracy of DNN accelerators. The model can handle the full chain from RRAM materials/device parameters to the DNN performance, thus providing a valuable tool for device/circuit codesign of hardware DNN accelerators.}, language = {en} } @misc{JanowitzMahmoodinezhadKotetal., author = {Janowitz, Christoph and Mahmoodinezhad, Ali and Kot, Małgorzata and Morales, Carlos and Naumann, Franziska and Plate, Paul and Z{\"o}llner, Marvin Hartwig and B{\"a}rwolf, Florian and Stolarek, David and Wenger, Christian and Henkel, Karsten and Flege, Jan Ingo}, title = {Toward controlling the Al2O3/ZnO interface properties by in situ ALD preparation}, series = {Dalton Transactions}, volume = {51}, journal = {Dalton Transactions}, issn = {1477-9234}, doi = {10.1039/D1DT04008A}, pages = {9291 -- 9301}, abstract = {An Al2O3/ZnO heterojunction was grown on a Si single crystal substrate by subsequent thermal and plasma-assisted atomic layer deposition (ALD) in situ. The band offsets of the heterointerface were then studied by consecutive removal of the layers by argon sputtering, followed by in situ X-ray photoelectron spectroscopy. The valence band maximum and conduction band minimum of Al2O3 are found to be 1.1 eV below and 2.3 eV above those of ZnO, resulting in a type-I staggered heterojunction. An apparent reduction of ZnO to elemental Zn in the interface region was detected in the Zn 2p core level and Zn L3MM Auger spectra. This suggests an interface formation different from previous models. The reduction of ZnO to Zn in the interface region accompanied by the creation of oxygen vacancies in ZnO results in an upward band bending at the interface. Therefore, this study suggests that interfacial properties such as the band bending as well as the valence and conduction band offsets should be in situ controllable to a certain extent by careful selection of the process parameters.}, language = {en} } @misc{PerezMahadevaiahPerezBoschQuesadaetal., author = {P{\´e}rez, Eduardo and Mahadevaiah, Mamathamba Kalishettyhalli and Perez-Bosch Quesada, Emilio and Wenger, Christian}, title = {In-depth characterization of switching dynamics in amorphous HfO2 memristive arrays for the implementation of synaptic updating rules}, series = {Japanese Journal of Applied Physics}, volume = {61}, journal = {Japanese Journal of Applied Physics}, issn = {0021-4922}, doi = {10.35848/1347-4065/ac6a3b}, pages = {1 -- 7}, abstract = {Accomplishing truly analog conductance modulation in memristive arrays is crucial in order to implement the synaptic plasticity in hardware-based neuromorphic systems. In this paper, such a feature was addressed by exploiting the inherent stochasticity of switching dynamics in amorphous HfO2 technology. A thorough statistical analysis of experimental characteristics measured in 4 kbit arrays by using trains of identical depression/potentiation pulses with different voltage amplitudes and pulse widths provided the key to develop two different updating rules and to define their optimal programming parameters. The first rule is based on applying a specific number of identical pulses until the conductance value achieves the desired level. The second one utilized only one single pulse with a particular amplitude to achieve the targeted conductance level. In addition, all the results provided by the statistical analysis performed may play an important role in understanding better the switching behavior of this particular technology.}, language = {en} }